def preprocess_raw_ica_only(sub_id, session):
""" This function removes the ICA component that correlates woth the
EOG channel(s) best.
No filtering or downsampling is applied!
"""
# SETUP AND LOAD FILES ####
# name with subject id & session name
fname = "sub_%d_%s" % (sub_id, session)
# load the raw fif
print '\nLoading raw file'
raw = fiff.Raw(fname + "_tsss_mc.fif", preload=True)
picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False, eog=False,
stim=False, exclude='bads')
# ICA ####
print '\nRun ICA'
ica = ICA(n_components=0.90, n_pca_components=64, max_pca_components=100,
noise_cov=None, random_state=0)
start, stop = None, None
# decompose sources for raw data
ica.decompose_raw(raw, start=start, stop=stop, picks=picks)
corr = lambda x, y: np.array([pearsonr(a, y.ravel()) for a in x])[:, 0]
eog_scores_1 = ica.find_sources_raw(raw, target='EOG001',
score_func=corr)
eog_scores_2 = ica.find_sources_raw(raw, target='EOG002',
score_func=corr)
# get maximum correlation index for EOG
eog_source_idx_1 = np.abs(eog_scores_1).argmax()
eog_source_idx_2 = np.abs(eog_scores_2).argmax()
# We now add the eog artifacts to the ica.exclusion list
if eog_source_idx_1 == eog_source_idx_2:
ica.exclude += [eog_source_idx_1]
elif eog_source_idx_1 != eog_source_idx_2:
ica.exclude += [eog_source_idx_1, eog_source_idx_2]
print eog_source_idx_1, eog_source_idx_2
print ica.exclude
# Restore sensor space data
raw_ica = ica.pick_sources_raw(raw, include=None)
# SAVE FILES ####
raw_ica.save(fname + '_tsss_mc_preproc_ica.fif', overwrite=True)
ica.plot_sources_raw(raw, eog_source_idx, title=title, stop=3.0)
# plot spatial sensitivities of EOG and ECG ICA components
title = 'Spatial patterns of ICA components for ECG+EOG (Magnetometers)'
source_idx = range(15)
ica.plot_topomap([ecg_source_idx, eog_source_idx], ch_type='mag')
plt.suptitle(title, fontsize=12)
###############################################################################
# Show MEG data before and after ICA cleaning.
# We now add the eog artifacts to the ica.exclusion list
ica.exclude += [eog_source_idx]
# Restore sensor space data and keep all PCA components
raw_ica = ica.pick_sources_raw(raw, include=None, n_pca_components=1.0)
# let's now compare the date before and after cleaning.
start_compare, stop_compare = raw.time_as_index([100, 106])
data, times = raw[picks, start_compare:stop_compare]
data_clean, _ = raw_ica[picks, start_compare:stop_compare]
# first the raw data
plt.figure()
plt.plot(times, data.T, color='r')
plt.plot(times, data_clean.T, color='k')
plt.xlabel('time (s)')
plt.xlim(100, 106)
plt.show()
# now the affected channel
ica.plot_sources_raw(raw, eog_source_idx, title=title, stop=3.0)
# plot spatial sensitivities of EOG and ECG ICA components
title = 'Spatial patterns of ICA components for ECG+EOG (Magnetometers)'
source_idx = range(15)
ica.plot_topomap([ecg_source_idx, eog_source_idx], ch_type='mag')
plt.suptitle(title, fontsize=12)
###############################################################################
# Show MEG data before and after ICA cleaning.
# We now add the eog artifacts to the ica.exclusion list
ica.exclude += [eog_source_idx]
# Restore sensor space data
raw_ica = ica.pick_sources_raw(raw, include=None)
start_compare, stop_compare = raw.time_as_index([100, 106])
data, times = raw[picks, start_compare:stop_compare]
data_clean, _ = raw_ica[picks, start_compare:stop_compare]
plt.figure()
plt.plot(times, data.T)
plt.xlabel('time (s)')
plt.xlim(100, 106)
plt.ylabel('Raw MEG data (T)')
y0, y1 = plt.ylim()
plt.figure()
plt.plot(times, data_clean.T)
ica.plot_sources_raw(raw, eog_source_idx, title=title, stop=3.0)
# plot spatial sensitivities of EOG and ECG ICA components
title = 'Spatial patterns of ICA components for ECG+EOG (Magnetometers)'
source_idx = range(15)
ica.plot_topomap([ecg_source_idx, eog_source_idx], ch_type='mag')
plt.suptitle(title, fontsize=12)
###############################################################################
# Show MEG data before and after ICA cleaning.
# We now add the eog artifacts to the ica.exclusion list
ica.exclude += [eog_source_idx]
# Restore sensor space data and keep all PCA components
raw_ica = ica.pick_sources_raw(raw, include=None, n_pca_components=1.0)
# let's now compare the date before and after cleaning.
start_compare, stop_compare = raw.time_as_index([100, 106])
data, times = raw[picks, start_compare:stop_compare]
data_clean, _ = raw_ica[picks, start_compare:stop_compare]
# first the raw data
plt.figure()
plt.plot(times, data.T, color='r')
plt.plot(times, data_clean.T, color='k')
plt.xlabel('time (s)')
plt.xlim(100, 106)
plt.show()
# now the affected channel
def preprocess_raw(sub_id, session):
""" This function preprocessess data
"""
# SETUP AND LOAD FILES ####
# name with subject id & session name
fname = "sub_%d_%s" % (sub_id, session)
# load the raw fif
print '\nLoading raw file'
raw = fiff.Raw(fname + "_tsss_mc.fif", preload=True)
picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False, eog=False,
stim=False, exclude='bads')
print 'Computing Covariance matrix'
cov = mne.compute_raw_data_covariance(raw, picks=picks, reject=None)
# FILTER ####
# filter raw, lp 128, bp at 50 & 100
raw.filter(None, 128, n_jobs=n_jobs, verbose=True)
# steps = np.arange(50, 151, 50)
# print '\nBand stop filter at %s' % steps
# raw.notch_filter(steps, n_jobs=n_jobs, verbose=True)
# ICA ####
print '\nRun ICA'
ica = ICA(n_components=0.90, n_pca_components=64, max_pca_components=100,
noise_cov=None, random_state=0)
start, stop = None, None
# decompose sources for raw data
ica.decompose_raw(raw, start=start, stop=stop, picks=picks)
corr = lambda x, y: np.array([pearsonr(a, y.ravel()) for a in x])[:, 0]
eog_scores_1 = ica.find_sources_raw(raw, target='EOG001',
score_func=corr)
eog_scores_2 = ica.find_sources_raw(raw, target='EOG002',
score_func=corr)
# get maximum correlation index for EOG
eog_source_idx_1 = np.abs(eog_scores_1).argmax()
eog_source_idx_2 = np.abs(eog_scores_2).argmax()
# We now add the eog artifacts to the ica.exclusion list
if eog_source_idx_1 == eog_source_idx_2:
ica.exclude += [eog_source_idx_1]
elif eog_source_idx_1 != eog_source_idx_2:
ica.exclude += [eog_source_idx_1, eog_source_idx_2]
print eog_source_idx_1, eog_source_idx_2
print ica.exclude
# Restore sensor space data
raw_ica = ica.pick_sources_raw(raw, include=None)
# EPOCHS ####
events = mne.find_events(raw_ica, stim_channel="STI101")
events_classic = []
events_interupt = []
for i in range(len(events)):
if i > 0:
if events[i, 2] == 1 and events[i - 1, 2] == 1:
events_classic.append(i)
elif events[i, 2] == 1 and events[i - 1, 2] == 2:
events_interupt.append(i)
picks = mne.fiff.pick_types(raw_ica.info, meg=True, eeg=False, eog=False,
emg=True, stim=False, exclude='bads')
reject = dict(grad=4000e-13)
epochs = mne.Epochs(raw_ica, events[events_classic], event_id, tmin, tmax,
proj=True, picks=picks, baseline=baseline,
preload=False, reject=reject)
# SAVE FILES ####
raw_ica.save(fname + '_tsss_mc_ica.fif', overwrite=True)
cov.save((fname + '_tsss_mc_cov.fif'))
epochs.save(fname + '_tsss_mc_ica_epochs.fif')
ica.plot_sources_raw(raw, eog_source_idx, title=title, stop=3.0)
# plot spatial sensitivities of EOG and ECG ICA components
title = 'Spatial patterns of ICA components for ECG+EOG (Magnetometers)'
source_idx = range(15)
ica.plot_topomap([ecg_source_idx, eog_source_idx], ch_type='mag')
plt.suptitle(title, fontsize=12)
###############################################################################
# Show MEG data before and after ICA cleaning.
# We now add the eog artifacts to the ica.exclusion list
ica.exclude += [eog_source_idx]
# Restore sensor space data
raw_ica = ica.pick_sources_raw(raw, include=None)
start_compare, stop_compare = raw.time_as_index([100, 106])
data, times = raw[picks, start_compare:stop_compare]
data_clean, _ = raw_ica[picks, start_compare:stop_compare]
plt.figure()
plt.plot(times, data.T)
plt.xlabel('time (s)')
plt.xlim(100, 106)
plt.ylabel('Raw MEG data (T)')
y0, y1 = plt.ylim()
plt.figure()
plt.plot(times, data_clean.T)
